Index: liacs/dbdm/dbdm_5/Makefile
===================================================================
--- liacs/dbdm/dbdm_5/Makefile	(revision 71)
+++ liacs/dbdm/dbdm_5/Makefile	(revision 71)
@@ -0,0 +1,23 @@
+# Rick van der Zwet
+# 0433373
+# XXX Assignment XXX
+# Licence: BSD
+# $Id: Makefile 741 2009-08-05 19:26:23Z rick $
+
+include latex.mk
+
+CFLAGS+=-Wall -g
+PROGRAMS=XXX
+
+all: $(PROGRAMS)
+
+clean: doc-clean
+	rm -Rf $(PROGRAMS)
+
+distclean: doc-distclean
+
+debug:
+	@sh -c 'while [ "true" ]; do make clean && make && ./${PROGRAMS}; echo -n "Completed: "; date; sleep 5; done'
+
+ones: $(PROGRAMS)
+	@sh -c ./$(PROGRAMS)
Index: liacs/dbdm/dbdm_5/latex.mk
===================================================================
--- liacs/dbdm/dbdm_5/latex.mk	(revision 71)
+++ liacs/dbdm/dbdm_5/latex.mk	(revision 71)
@@ -0,0 +1,61 @@
+# Rick van der Zwet <rick@wzoeterwoude.net>
+# Licence: BSD
+# Descrip: LaTex file parser
+# $Id: latex.mk 571 2008-04-20 17:31:04Z rick $
+
+TARGET = report
+LATEX = latex
+XDVI = xdvi
+TEX = latex -halt-on-error
+DVIPDF = dvipdf
+ASPELL = aspell
+ASPELL_ARGS = -c --mode=tex -x
+
+#XXX: Make me dynamic
+VIEWPDF = evince
+.SUFFIXES: .pdf
+
+help:
+	@echo "Usage Makefile <option>"
+
+doc-all: doc-distclean buildpdf
+
+view: viewpdf
+
+viewdvi: builddvi
+	$(XDVI) $(TARGET)
+
+builddvi: $(SOURCE) $(TARGET).dvi
+
+doc-clean:
+	rm -vf *.aux *.log *toc *.out *.bak *.m.tex
+
+doc-distclean: doc-clean
+	rm -vf $(TARGET).dvi $(TARGET).pdf $(PROGRAM)
+
+buildpdf: $(SOURCE) $(TARGET).pdf
+
+viewpdf: buildpdf
+	$(VIEWPDF) $(TARGET).pdf
+
+spell: spell-en
+
+spell-nl: $(TARGET).tex
+	$(ASPELL) -l nl -p ~/aspell-n.dic $(ASPELL_ARGS) $<
+
+spell-en: $(TARGET).tex
+	$(ASPELL) -l en -p ~/aspell-en.dic $(ASPELL_ARGS) $<
+
+.dvi.pdf:
+	$(DVIPDF) $? $@
+
+.tex.dvi:
+	$(TEX) $? $@
+	$(TEX) $? $@
+
+.gs.png:
+	$(GNUPLOT) $?
+
+%.m.tex: %.m
+	highlight --include-style --linenumbers --no-doc \
+	  --latex --input $< --output $@
Index: liacs/dbdm/dbdm_5/report.tex
===================================================================
--- liacs/dbdm/dbdm_5/report.tex	(revision 71)
+++ liacs/dbdm/dbdm_5/report.tex	(revision 71)
@@ -0,0 +1,74 @@
+\documentclass{report}
+
+\title{Databases and Data Mining --- Assignment 5}
+\author{Rick van der Zwet}
+
+
+\begin{document}
+\newcommand{\question}[2]{\begin{quotation}\noindent{\Large``}#1. #2{\Large''}\end{quotation}}
+\maketitle
+
+\question{1a}{Propose an algorithm, in pseudo-code, for the following: The automatic
+generation of a concept hierarchy for numerical data based on the equal-width
+partitioning rule.}
+
+\question{1b}{Propose an algorithm, in pseudo-code, for the following: The automatic
+generation of a concept hierarchy for numerical data based on the equal-frequency
+partitioning rule.}
+
+\question{2}{Suppose that a data warehouse consists of four dimensions,
+    date, spectator, location, and game, and two measures, count, and charge, where
+    charge is the fare that a spectator pays when watching a game on a given date.
+    Spectators may be students, adults, or seniors, with each category having its own
+    charge rate.}
+\question{2a}{Draw a star schema diagram for the data warehouse.}
+\question{2b}{Starting with the base cuboid [date, spectator, location, game], what specific
+        OLAP operations should one perform in order to list the total charge paid by
+        student spectators at GM\_Place 2004?}
+\question{2c}{Bitmap indexing is useful in data warehousing. Taking this cube as an example,
+        briefly discuss advantages and problems of using a bitmap index structure.}
+
+\question{3}{A data cube, C, has n dimensions, and each dimension
+    has exactly p distinct values in the base cuboid. Assume that there are no concept
+    hierarchies associated with the dimensions. What is the maximum number of cells
+    possible (including both base cells and aggregate cells) in the data cube, C?}
+
+\question{4}{The Apriori algorithm uses prior knowledge of subset support properties.}
+\question{4a}{Prove that all nonempty subsets of a frequent itemset must also be frequent.}
+\question{4b}{Given frequent itemset l and subset s of l, prove that the confidence of the rule âsâ
+         => (l-sâ)â cannot be more than the confidence of the rule âs => (l â s)â where sâ
+         is a subset of s.}
+\question{5}{An optimization in frequent item set mining is mining closed patterns, or mining max
+    patterns instead. Describe the main differences of mining closed patterns and mining
+    max patterns.}
+
+\question{6}{The price of each item in a store is nonnegative. For
+each of the following cases, identify the kinds of constraint they represent (e.g.
+antimonotonic, monotonic, succinct) and briefly discuss how to mine such association
+rules efficiently:}
+
+\question{6a}{Containing one free item and other items the sum of whose prices is at least \$190.}
+\question{6b}{Where the average price of all the items is between \$120 and \$520.}
+
+\question{7}{Suppose a city has installed hundreds of surveillance cameras at strategic locations in
+busy streets. All the captured video (each stream being 640 pixels x 480 pixels, 24-
+bits (RGB) per pixel, 25fps) is streamed to a central observation post in real time.
+Describe an efficient system that would allow real time data mining and continuously
+querying these video streams for abnormal events, such as explosions, people in
+panic, etc.. Also discuss the computational costs and the memory requirements of
+your system.}
+\question{8}{A flight data warehouse for a travel agent consists of six
+dimensions: traveler, departure (city), departure\_time, arrival (city), arrival\_time,
+and flight; and two measures count, and avg\_fare, where avg\_fare stores the concrete
+fare at the lowest level but the average fare at the other levels. Suppose the cube is
+fully materialized. Starting with the base cuboid [traveler, departure, departure\_time,
+arrival, arrival\_time, flight], what specific OLAP operations (e.g roll-up flight to
+airline, etc.) should one perform in order to list the average fare per month for each
+business traveler who flies American Airlines (AA) from Los Angeles (LA) in the
+year 2007?}
+\question{9}{In graph mining what would be the advantage of the described apriori-based approach
+over the pattern growth based approach (see lecture slides) and vice versa.}
+
+\bibliography{report}
+
+\end{document}
